Wang  [1]} recently published the first benchmark dataset for open-set instance segmentation, which includes various categories from YouTube videos. However, from a methodological perspective, open-world object detection and segmentation remain understudied despite the importance of the task.
Hu and Kuo  [2]}, [3]} proposed approaches for predicting masks of various objects, but they require bounding boxes from classes of interest.
Jaiswal  [4]} trained a detector in an adversarial manner to learn class-invariant objectness.
Joseph  [5]} proposed a semi-supervised learning approach for open-world detection, which regards regions that are far from ground truth boxes but have a high objectness score as hidden foreground objects.
Kim [6]} employed localization quality estimation with the claim that the estimation strategy is more generalizable in open-world instance segmentation.
